Operations Research Scientist / Optimization Engineer

Cognizant; United States; On-site; USD 120,000 - 170,000; Full time

Benefits offered by this job

Remote work

Job summary

Cognizant is seeking an Operations Research Scientist / Optimization Engineer to architect and productionize algorithmic solutions for enterprise-scale retail operations, including MILP, nonlinear, and stochastic models for omnichannel supply chains and routing. You'll bridge theory with cloud data warehousing and AI platforms, collaborating with MLOps, Data Engineering, and product teams to deploy low-latency pipelines.

PhD or MS with strong research/internship backgrounds welcomed.

Qualifications

• Ph.D. or M.Tech / M.S. in Operations Research, Industrial Engineering, Applied Mathematics, Computer Science, Systems Engineering, or related quantitative field.
• Recent Ph.D. graduates with thesis focus on mathematical programming / OR or 1+ years relevant industry/internship experience.
• 2+ years of industry experience applying optimization models in supply chain, logistics, or retail (strong graduate thesis/internship portfolios for new graduates).
• Deep hands-on experience with commercial and open-source solvers (Gurobi, CPLEX, FICO Xpress, OR-Tools, SCIP, PuLP, Pyomo).
• Advanced proficiency in Python (NumPy, SciPy, Pandas, NetworkX) or C++/Julia/Java.
• Solid foundation in LP, MILP, decomposition techniques (Benders, Dantzig-Wolfe, Column Generation), meta-heuristics, and graph theory.

Responsibilities

• Mathematical Formulation & Optimization: Formulate and solve large-scale MILP, nonlinear, stochastic, and dynamic network optimization models.
• Retail & Omnichannel Supply Chain: Design algorithmic solutions for safety stock sizing, forward deployment, store replenishment, omnichannel sourcing, and cross-dock scheduling.
• AI & Data Science Synergy: Develop hybrid models that fuse ML/DL with deterministic/stochastic optimization solvers.
• Simulation & Scenario Modeling: Build discrete-event simulation engines and digital twins to stress-test network resilience and seasonal volatility.
• Production Deployment & Scaling: Partner with MLOps, Data Engineering, and Product teams to scale algorithms into low-latency production pipelines and microservices on cloud.
• Cross-Functional Collaboration: Translate constraints from Merchandising, Store Ops, Logistics, and Finance into mathematical constraints and decision engines.

Skills

Mathematical optimization

Quantitative analysis

Python programming

Team collaboration

Education

Ph.D. in Operations Research

M.Tech / M.S. in OR / Industrial Engineering / Applied Mathematics / Computer Science

Tools

Gurobi

CPLEX

FICO Xpress

Google OR-Tools

SCIP

PuLP

Pyomo

Python

C++/Julia/Java

Job description

Job Title- Operations Research Scientist / Optimization Engineer

**remote**

1. About the Role

We are looking for an exceptional Operations Research Scientist / Optimization Engineer to architect, build, and productionize algorithmic solutions for enterprise-scale retail operations (exp with- omnichannel supply chain). In this role, you will apply mathematical programming, large-scale heuristics, and AI/Data Science techniques to solve critical supply chain optimization problems, including multi-echelon inventory placement, omnichannel order fulfillment, distribution center robotics/flow scheduling, dynamic markdown & pricing, and middle-to-last-mile transportation routing.

This position offers an opportunity to bridge theoretical optimization with modern cloud data warehousing and enterprise AI platforms. We welcome applications from candidates with a Ph.D. or M.Tech/M.S. in Operations Research, Industrial Engineering, Applied Mathematics, or Computer Science—ranging from fresh graduates with strong research/internship foundations to seasoned industry practitioners.

2. Key Responsibilities

• Mathematical Formulation & Optimization: Formulate and solve large-scale Mixed-Integer Linear Programming (MILP), non-linear, stochastic, and dynamic network optimization models.
• Retail & Omnichannel Supply Chain: Design algorithmic solutions for safety stock sizing, forward deployment, store replenishment, omnichannel order sourcing, and cross-dock cross-flow scheduling.
• AI & Data Science Synergy: Develop hybrid models that fuse Machine Learning/Deep Learning (for demand uncertainty & lead-time forecasting) with deterministic/stochastic optimization solvers.
• Simulation & Scenario Modeling: Build discrete-event simulation engines and digital twins to stress-test network resilience, DC automation throughput, and seasonal volatility (e.g., peak holiday surges).
• Production Deployment & Scaling: Partner with MLOps, Data Engineering, and Product teams to scale algorithms into low-latency production pipelines and microservices on cloud infrastructure.
• Cross-Functional Collaboration: Translate complex business constraints from Merchandising, Store Operations, Logistics, and Finance into mathematical constraints and executive decision engines.

3. Required Qualifications

• Education: Ph.D. or M.Tech / M.S. in Operations Research, Industrial Engineering, Applied Mathematics, Computer Science, Systems Engineering, or related quantitative field.
• Experience (Ph.D. Track): Recent Ph.D. graduates with thesis focus on mathematical programming / OR or 1+ years relevant industry/internship experience.
• Experience (M.Tech / M.S. Track): 2+ years of industry experience applying optimization models in supply chain, logistics, or retail (strong graduate thesis/internship portfolios considered for recent graduates).
• Optimization Solvers: Deep hands-on experience with commercial and open-source mathematical solvers (e.g., Gurobi, CPLEX, FICO Xpress, Google OR-Tools, SCIP, PuLP, Pyomo).
• Core Programming: Advanced proficiency in Python (NumPy, SciPy, Pandas, NetworkX) or C++/Julia/Java.
• Algorithmic Foundation: Solid foundation in LP, MILP, decomposition techniques (Benders, Dantzig-Wolfe, Column Generation), meta-heuristics, and graph theory.